Direction-of-Arrival Estimation for Terahertz Massive MIMO Systems under Non-Stationary Noise Environment

The Terahertz band supports ultra-wide bandwidth and ultra-high-speed transmission rates. Hybrid dynamic array-of-subarrays (DAoSA) beamforming in massive multiple-input multiple-output (mMIMO) systems offers advantages such as high beamforming gain and low hardware complexity. It is a crucial factor in achieving milli-degree accuracy in estimating the direction-of-arrival (DOA). However, in non-stationary noise environments, the various subcarriers of ultra-wideband signals may encounter beam-squint issues, which present significant challenges for DOA estimation. Addressing this challenge, this paper investigates the DOA estimation problem in Terahertz mMIMO systems employing DAoSA architecture. The Pre-Whitening noise and Beam-squint Canceller method for Multiple Signal Classification (PWBC-MUSIC) has been proposed. Initially, it employs pre-whitening of the array data’s covariance matrix using an estimated noise covariance matrix. Subsequently, it employs a linear transformation matrix to mitigate beam-squint. Furthermore, the paper includes derivation of the Cramér-Rao lower bound (CRLB) to substantiate the algorithm’s performance. The simulation results demonstrate that the proposed algorithm can effectively suppress the interference of non-stationary noise, particularly at low signal-to-noise ratios, and can achieve milli-degree level DOA estimation under ultra-wideband conditions.